The way digital news platforms represent migration issues can signifi-
cantly impact intergroup relations and policymaking. A recurring question in the
debate on the role of news platforms is whether they merely transmit informa-
tion on migration, or actively hype specific issues. Drawing on a comprehensive
set of socioeconomic statistics on migrants in Denmark, and employing a longitu-
dinal automated content analysis of migration news content, we utilize time-series
analysis to understand how four distinct categories of threat (security, economic,
cultural, and generalized) relate to socioeconomic data on terror attacks, migrant
crime levels, economic performance, and demographic trends. The results reveal a
direct effect of terror attacks, economic performance, and demographic trends on
migration news. We discuss the implications of socioeconomic and demographic
developments as factors in digital media content to understand the role of media
and substantiate contemporary debates

Central and Eastern Europe (CEE) countries faced significant political, economic, social, and technological transformations over the last four decades. Democratic processes, after relative stabilization, tremble again around polarizing values, populist leaders, or nationalistic ideologies. Online communication, especially social media platforms, play a vital role in shaping how citizens interact with the state, political actors, media, and other citizens. The book focuses on some of the challenges democratic institutions in the region face, in transforming and sustaining civil society and attempts to capture how the digital media environments mitigate or exacerbate those challenges. Included manuscripts focus on the role that online platforms play in the satisfaction with democracy in the CEE region, the interactions between journalists and political actors, the strategic media coverage of elections, affective polarization and political antagonism, and discursive attempts to discourage young people from civic engagement.

Journalists and experts play a pivotal role in communicating risks and helping the public navigate uncertain futures. This study examines the co-construction of projections by journalists and experts across news and social media during the Covid-19 pandemic. Unlike traditional news production, where journalists exercise agency by transforming expert knowledge into news narratives, hybrid media environments involve multi-platform, multi-directional, and non-linear processes of knowledge production. In light of these characteristics, we introduce and develop the concept of “predictive agency,” referring to an actor’s active participation in predictive knowledge-making and encompassing journalistic, civic, and epistemic forms of agency in shaping and navigating future-oriented knowledge. We analyse the trajectories of 400 projections in Israel and the US, tracing the interactional and informational dynamics between journalists and experts. Through qualitative textual analysis of the various iterations of each projection, four types of co-constructed projection systems emerge: Amplify, Distill, Elaborate, and Contest. We explore the complexities of predictive agency and accountability in these systems, shedding light on how collective futures are contested and co-constructed in hybrid media environments.

Communication research has long explored the association between media trust and news consumption. However, the strength and direction of this relationship have remained elusive. This study suggests a new approach for investigating these complex relations, differentiating between usage and trust associated with different sources over time. Focusing on the 2022 French election and drawing on data from a four-wave panel survey (N = 1,294), we utilized Random Intercept Cross-Lagged Panel Model (RI-CLPM) analysis to uncover two key over time effects: a selection effect, wherein trust reinforces usage; and a media effect, wherein usage influences trust. While a selection effect driven by news trust was observed in a right-wing political alternative channel, a media effect leading to news trust was linked with more traditional television channels. By identifying these effects and their associations with various types of outlets, this study advances the ongoing scholarly debate around the role of trust in news consumption.

This study investigates how elected representatives make promises to the electorate as part of their routine, everyday parliamentary discourse. Departing from the dominant focus on pre-election pledges in existing scholarship, we examine how representatives’ practices of making promises vary systematically over time (before/after elections/mid-term), by government role (government/opposition), party membership (catch-all/identity-based parties), and addressed electorate (inclusive/exclusive). Drawing upon research in speech acts, political discourse, and corpus pragmatics, we employ manual and computational text analysis on session transcripts from the Israeli parliament to identify variations in representatives’ use of direct or indirect action promises (N = 709), expressing different orientations of commitment. Documenting that promises continue to be prevalent after elections (especially among coalition members), we argue that the practices of making promises fulfill a critical role in representation that extends far beyond the election campaign, maintaining continuous accountability throughout the electoral cycle.

How do religious citizens’ election projections influence voter turnout? While previous studies have demonstrated the significant impact of religious orientation on individuals’ general future outlook, little is known about the influence of religion on voters’ electoral expectations and how these expectations affect voter turnout. In this paper, we employ a nuanced conceptual framework of election projections and examine the impact of religion on both the affective and probabilistic aspects of citizens’ expectations regarding election outcomes. Our analysis draws upon original panel survey data collected in two countries, focusing on the 2021 Israeli general elections and the 2022 French presidential elections. The findings reveal a mobilizing effect of religious citizens’ election projections in both Israel and France. Specifically, religious voters tend to have more positive affective forecasts about their projected election outcomes, consequently resulting in increased voter turnout. While affective forecasting plays a significant role in religious citizens’ turnout, probabilistic certitude does not have a similar effect. We discuss the contribution and implications of these findings for research on religion and political behavior.

We introduce and implement a novel dimension-reduction method for high-dimensional time-varying contingency-tables: the Evolutionary Correspondence Analysis (ECA). ECA enables a comparative analysis of high-dimensional, diachronic processes by identifying a small number of shared latent variables that shape co-evolving data patterns. ECA offers new opportunities for the study of complex social phenomena, such as co-evolving public debates: Its capacity to inductively extract time-varying latent variables from observed contents of evolving debates permits an analysis of meanings shared by linked sub-discourses, such as linked national public spheres or the discourses led by distinct political camps within a shared public sphere. We illustrate the utility of our approach by studying how the Greek and German right-, centre-, and left-leaning news coverage of the European financial crisis evolved between its outbreak in 2009 until its institutional containment in 2012. Comparing the use of 525 unique concepts in six German and Greek outlets with different political leaning over an extended period of time, we identify two common factors accounting for those evolving meanings and analyse how the different sub-discourses influenced one another over time. We allow the factor loadings to be time-varying, and fit to the latent factors a time-varying vector-auto-regressive model with time-varying mean.

CEE countries faced significant political, economic, social, and technological transformations over the last four decades. Democratic processes, after relative stabilization, tremble again around polarizing values, populist leaders, or nationalistic ideologies. Online communication, especially social media platforms, play a vital role in shaping how citizens interact with the state, political actors, media, and other citizens. The collection of manuscripts focuses on some of the challenges democratic institutions in the region face, in transforming and sustaining civil society and attempts to capture how the digital media environments mitigate or exacerbate those challenges. Included manuscripts focus on the role that online platforms play in the satisfaction with democracy in the CEE region, the interactions between journalists and political actors, the strategic media coverage of elections, affective polarization and political antagonism, and discursive attempts to discourage young people from civic engagement.

In this paper, we propose a new strategy for classifying evaluations in large text corpora, using supervised machine learning (SML). Departing from a conceptual and methodological critique of the use of sentiment measures to recognize object-specific evaluations, we argue that a key challenge consists in determining whether a semantic relationship exists between evaluative expressions and evaluated objects. Regarding sentiment terms as merely potentially evaluative expressions, we thus use a SML classifier to decide whether recognized terms have an evaluative function in relation to the evaluated object. We train and test our classifier on a corpus of 10,004 segments of election coverage from 16 major U.S. news outlets and Tweets by 10 prominent U.S. politicians and journalists. Specifically, we focus on evaluations of political predictions about the outcomes and implications of the 2016 and 2020 U.S. presidential elections. We show that our classifier consistently outperforms both off-the-shelf sentiment tools and a pre-trained transformer-based sentiment classifier. Critically, our classifier correctly discards numerous non-evaluative uses of common sentiment terms, whose inclusion in conventional analyses generates large amounts of false positives. We discuss contributions of our approach to the measurement of object-specific evaluations and highlight challenges for future research.

This paper explores trust-building strategies in future-oriented news discourse, marked by a high degree of uncertainty. While current research mainly focuses on audiences’ perceptions of news credibility, this study addresses news trust from a production standpoint. We examine the trust-building efforts of media actors, focusing on their discursive labor within the context of election projections. Drawing on rich data from five election rounds in Israel and the US, we qualitatively analyzed 400 news texts and 400 tweets that were produced by 20 US and 20 Israeli media actors. This textual analysis was supplemented by 10 in-depth interviews with Israeli journalists. Our findings demonstrate three types of journalistic trust-building rhetoric in election coverage: facticity, authority, and transparency. These strategies result in a two-fold form of trust, which re-affirms traditional notions of accuracy and validity, while also challenging the ability of newspersons to obtain them in contemporary political and media cultures. Overall, these strategies hold unique opportunities and challenges for sustaining public trust in journalism and illuminate the complex communicative labor involved in building trust with news audiences. Our findings also highlight the importance of studying trust not only in relation to the past and the present, but also in future-oriented discourse.

In quantitative content analysis, conventional wisdom holds that reliability, operationalized as agreement, is a necessary precondition for validity. Underlying this view is the assumption that there is a definite, unique way to correctly classify any instance of a measured variable. In this intervention, we argue that there are textual ambiguities that cause disagreement in classification that is not measurement error, but reflects true properties of the classified text. We introduce a notion of valid disagreement, a form of replicable disagreement that must be distinguished from replication failures that threaten reliability. We distinguish three key forms of meaning multiplicity that result in valid disagreement – ambiguity due to under-specification, polysemy due to excessive information, and interchangeability of classification choices – that are widespread in textual analysis, yet defy treatment within the confines of the existing content-analytic toolbox. Discussing implications, we present strategies for addressing valid disagreement in content analysis.

As long as we have attempted to sanction untoward speech, others have devised strategies for expressing themselves while dodging such sanctions. In this intervention, I review the arms race between technological filters designed to curb hate speech, and evasive language practices designed to avoid detection by these filters. I argue that, following important advances in the detection of relatively overt uses of hate speech, further advances will need to address hate speech that relies on culturally or situationally available context knowledge and linguistic ambiguities to convey its intended offenses. Resolving such forms of hate speech not only poses increasingly unreasonable demands on available data and technologies, but does so for limited, uncertain gains, as many evasive uses of language effectively defy unique valid classification.

Focus group methods specialize in the analysis of interactive discourse, but are only rarely employed as a stand-alone method to study such phenomena, owing to inherent limitations concerning the comparability and generalizability of findings. In this paper, we argue that focus groups undergo three kinds of transformations, involving changes in participants’ cognitive states, social ties, and discursive behavior, which raise both analytic challenges and valuable opportunities for the study of shared meanings and interactive negotiation processes in society. Introducing Serial Focus Groups, we extend familiar focus group designs as a method for studying interactive discourse in a longitudinal perspective, capitalizing on the analytic potentials raised by these transformations. Reviewing the methodological literature and drawing upon two large-scale focus group studies of socially interactive sense-making, we argue that serial focus groups can help overcome some of the limitations of cross-sectional focus groups and offer valuable new opportunities for analysis and validation.

In orienting themselves to the future, people form expectations not only on what will happen but also on how they will feel about possible future occurrences. So far, such affective forecasting – the prediction of future feelings – has been studied mainly from a psychological perspective. This study aims to show the importance of a socio-communicative perspective for understanding the predictors, manifestations, and consequences of affective forecasting, especially when collective futures are at stake. Using the case study of the 2019-2021 Israeli elections and a combination of a twelve-wave survey and twenty-five focus groups, we show how political affective forecasts are associated with socio-communicative factors, are used in social interactions, and drive political polarization and participation. We conclude with a discussion of the implications of our findings for future research on affective forecasting in communication studies.

We identify three gaps that limit the utility and obstruct the progress of computational text analysis methods (CTAM) for social science research. First, we contend that CTAM development has prioritized technological over validity concerns, giving limited attention to the operationalization of social scientific measurements. Second, we identify a mismatch between CTAMs’ focus on extracting specific contents and document-level patterns, and social science researchers’ need for measuring multiple, often complex contents in the text. Third, we argue that the dominance of English language tools depresses comparative research and inclusivity toward scholarly communities examining languages other than English. We substantiate our claims by drawing upon a broad review of methodological work in the computational social sciences, as well as an inventory of leading research publications using quantitative textual analysis. Subsequently, we discuss implications of these three gaps for social scientists’ uneven uptake of CTAM, as well as the field of computational social science text research as a whole. Finally, we propose a research agenda intended to bridge the identified gaps and improve the validity, utility, and inclusiveness of CTAM.

The goal of this paper is to evaluate two methods for the topic modeling of multilingual document collections: (1) machine translation (MT), and (2) the coding of semantic concepts using a multilingual dictionary (MD) prior to topic modeling. We empirically assess the consequences of these approaches based on both a quantitative comparison of models and a qualitative validation of each method’s comparative potentials and weaknesses. Our case study uses two text collections (of tweets and news articles) in three languages (English, Hebrew, Arabic), covering the ongoing local conflicts between Israeli authorities, settlers and Palestinian Bedouins in the West Bank. We find that both methods produce a large share of equivalent topics, especially in the context of fairly regular news discourse, yet show limited but systematic differences when applied to highly variable social media discourse. While the MD model delivers a more nuanced picture of conflict-related topics, it misses several more peripheral topics, especially those unrelated to the dictionary’s focus, which are picked up by the MT model. Our study is a first step towards instrument validation, indicating that both methods yield valid, comparable results, while method-specific differences remain.